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feat: Add support for Conversatsions in Responses API

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Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo 2025-10-08 15:51:11 -04:00
parent 548ccff368
commit 1e59793288
18 changed files with 662 additions and 10 deletions
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1
llama_stack/providers/inline/agents/meta_reference/__init__.py
View file

@ -21,6 +21,7 @@ async def get_provider_impl(config: MetaReferenceAgentsImplConfig, deps: dict[Ap
deps[Api.safety],
deps[Api.tool_runtime],
deps[Api.tool_groups],
deps[Api.conversations],
policy,
Api.telemetry in deps,
)

6
llama_stack/providers/inline/agents/meta_reference/agents.py
View file

@ -30,6 +30,7 @@ from llama_stack.apis.agents import (
)
from llama_stack.apis.agents.openai_responses import OpenAIResponseText
from llama_stack.apis.common.responses import PaginatedResponse
from llama_stack.apis.conversations import Conversations
from llama_stack.apis.inference import (
Inference,
ToolConfig,
@ -63,6 +64,7 @@ class MetaReferenceAgentsImpl(Agents):
safety_api: Safety,
tool_runtime_api: ToolRuntime,
tool_groups_api: ToolGroups,
conversations_api: Conversations,
policy: list[AccessRule],
telemetry_enabled: bool = False,
):
@ -72,6 +74,7 @@ class MetaReferenceAgentsImpl(Agents):
self.safety_api = safety_api
self.tool_runtime_api = tool_runtime_api
self.tool_groups_api = tool_groups_api
self.conversations_api = conversations_api
self.telemetry_enabled = telemetry_enabled
self.in_memory_store = InmemoryKVStoreImpl()
@ -88,6 +91,7 @@ class MetaReferenceAgentsImpl(Agents):
tool_runtime_api=self.tool_runtime_api,
responses_store=self.responses_store,
vector_io_api=self.vector_io_api,
conversations_api=self.conversations_api,
)
async def create_agent(
@ -325,6 +329,7 @@ class MetaReferenceAgentsImpl(Agents):
model: str,
instructions: str | None = None,
previous_response_id: str | None = None,
conversation: str | None = None,
store: bool | None = True,
stream: bool | None = False,
temperature: float | None = None,
@ -339,6 +344,7 @@ class MetaReferenceAgentsImpl(Agents):
model,
instructions,
previous_response_id,
conversation,
store,
stream,
temperature,

116
llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py
View file

@ -24,6 +24,12 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseText,
OpenAIResponseTextFormat,
)
from llama_stack.apis.common.errors import (
ConversationNotFoundError,
InvalidConversationIdError,
)
from llama_stack.apis.conversations import Conversations
from llama_stack.apis.conversations.conversations import ConversationItem
from llama_stack.apis.inference import (
Inference,
OpenAIMessageParam,
@ -61,12 +67,14 @@ class OpenAIResponsesImpl:
tool_runtime_api: ToolRuntime,
responses_store: ResponsesStore,
vector_io_api: VectorIO, # VectorIO
conversations_api: Conversations,
):
self.inference_api = inference_api
self.tool_groups_api = tool_groups_api
self.tool_runtime_api = tool_runtime_api
self.responses_store = responses_store
self.vector_io_api = vector_io_api
self.conversations_api = conversations_api
self.tool_executor = ToolExecutor(
tool_groups_api=tool_groups_api,
tool_runtime_api=tool_runtime_api,
@ -205,6 +213,7 @@ class OpenAIResponsesImpl:
model: str,
instructions: str | None = None,
previous_response_id: str | None = None,
conversation: str | None = None,
store: bool | None = True,
stream: bool | None = False,
temperature: float | None = None,
@ -221,11 +230,22 @@ class OpenAIResponsesImpl:
if shields is not None:
raise NotImplementedError("Shields parameter is not yet implemented in the meta-reference provider")
if conversation is not None:
if not conversation.startswith("conv_"):
raise InvalidConversationIdError(conversation)
conversation_exists = await self._check_conversation_exists(conversation)
if not conversation_exists:
raise ConversationNotFoundError(conversation)
input = await self._load_conversation_context(conversation, input)
stream_gen = self._create_streaming_response(
input=input,
model=model,
instructions=instructions,
previous_response_id=previous_response_id,
conversation=conversation,
store=store,
temperature=temperature,
text=text,
@ -270,6 +290,7 @@ class OpenAIResponsesImpl:
model: str,
instructions: str | None = None,
previous_response_id: str | None = None,
conversation: str | None = None,
store: bool | None = True,
temperature: float | None = None,
text: OpenAIResponseText | None = None,
@ -296,7 +317,7 @@ class OpenAIResponsesImpl:
)
# Create orchestrator and delegate streaming logic
response_id = f"resp-{uuid.uuid4()}"
response_id = f"resp_{uuid.uuid4()}"
created_at = int(time.time())
orchestrator = StreamingResponseOrchestrator(
@ -327,5 +348,98 @@ class OpenAIResponsesImpl:
messages=orchestrator.final_messages,
)
if conversation and final_response:
await self._sync_response_to_conversation(conversation, all_input, final_response)
async def delete_openai_response(self, response_id: str) -> OpenAIDeleteResponseObject:
return await self.responses_store.delete_response_object(response_id)
async def _check_conversation_exists(self, conversation_id: str) -> bool:
"""Check if a conversation exists."""
try:
await self.conversations_api.get_conversation(conversation_id)
return True
except ConversationNotFoundError:
return False
async def _load_conversation_context(
self, conversation_id: str, content: str | list[OpenAIResponseInput]
) -> list[OpenAIResponseInput]:
"""Load conversation history and merge with provided content."""
try:
conversation_items = await self.conversations_api.list(conversation_id, order="asc")
context_messages = []
for item in conversation_items.data:
if isinstance(item, OpenAIResponseMessage):
if item.role == "user":
context_messages.append(
OpenAIResponseMessage(
role="user", content=item.content, id=item.id if hasattr(item, "id") else None
)
)
elif item.role == "assistant":
context_messages.append(
OpenAIResponseMessage(
role="assistant", content=item.content, id=item.id if hasattr(item, "id") else None
)
)
# add new content to context
if isinstance(content, str):
context_messages.append(OpenAIResponseMessage(role="user", content=content))
elif isinstance(content, list):
context_messages.extend(content)
return context_messages
except Exception as e:
logger.error(f"Failed to load conversation context for {conversation_id}: {e}")
if isinstance(content, str):
return [OpenAIResponseMessage(role="user", content=content)]
return content
async def _sync_response_to_conversation(
self, conversation_id: str, content: str | list[OpenAIResponseInput], response: OpenAIResponseObject
) -> None:
"""Sync content and response messages to the conversation."""
try:
conversation_items = []
# add user content message(s)
if isinstance(content, str):
conversation_items.append(
{"type": "message", "role": "user", "content": [{"type": "input_text", "text": content}]}
)
elif isinstance(content, list):
for item in content:
if isinstance(item, OpenAIResponseMessage) and item.role == "user":
if isinstance(item.content, str):
conversation_items.append(
{
"type": "message",
"role": "user",
"content": [{"type": "input_text", "text": item.content}],
}
)
elif isinstance(item.content, list):
conversation_items.append({"type": "message", "role": "user", "content": item.content})
# add assistant response message
for output_item in response.output:
if isinstance(output_item, OpenAIResponseMessage) and output_item.role == "assistant":
if hasattr(output_item, "content") and isinstance(output_item.content, list):
conversation_items.append(
{"type": "message", "role": "assistant", "content": output_item.content}
)
if conversation_items:
adapter = TypeAdapter(list[ConversationItem])
validated_items = adapter.validate_python(conversation_items)
await self.conversations_api.add_items(conversation_id, validated_items)
except Exception as e:
logger.error(f"Failed to sync response {response.id} to conversation {conversation_id}: {e}")
# don't fail response creation if conversation sync fails
return None